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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 269] [Impact Index Per Article: 89.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Cotrina EY, Blasi D, Vilà M, Planas A, Abad-Zapatero C, Centeno NB, Quintana J, Arsequell G. Optimization of kinetic stabilizers of tetrameric transthyretin: A prospective ligand efficiency-guided approach. Bioorg Med Chem 2020; 28:115794. [DOI: 10.1016/j.bmc.2020.115794] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 12/15/2022]
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Srinivas R, Klimovich PV, Larson EC. Implicit-descriptor ligand-based virtual screening by means of collaborative filtering. J Cheminform 2018; 10:56. [PMID: 30467684 PMCID: PMC6755561 DOI: 10.1186/s13321-018-0310-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/13/2018] [Indexed: 12/20/2022] Open
Abstract
Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands’ structural and physicochemical properties in a vectorized form. Of particular importance to current methods are the extent to which molecular fingerprints describe a particular ligand and what metric sufficiently captures similarity among ligands. In this work, we propose and evaluate methods that do not require explicit feature vectorization through fingerprinting, but, instead, provide implicit descriptors based only on other known assays. Our methods are based upon well known collaborative filtering algorithms used in recommendation systems. Our implicit descriptor method does not require any fingerprint similarity search, which makes the method free of the bias arising from the empirical nature of the fingerprint models. We show that implicit methods significantly outperform traditional machine learning methods, and the main strengths of implicit methods are their resilience to target-ligand sparsity and high potential for spotting promiscuous ligands.
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Affiliation(s)
- Raghuram Srinivas
- Department of Computer Science and Engineering, Bobby B. Lyle School of Engineering, Southern Methodist University, 3145 Dyer Street, Dallas, TX, 75205, USA. .,DataScience@SMU, Dallas, 75205, TX, USA.
| | - Pavel V Klimovich
- Department of Computer Science and Engineering, Bobby B. Lyle School of Engineering, Southern Methodist University, 3145 Dyer Street, Dallas, TX, 75205, USA.,The Dedman College Interdisciplinary Institute, 3225 Daniel Avenue, Dallas, TX, 75205, USA
| | - Eric C Larson
- Department of Computer Science and Engineering, Bobby B. Lyle School of Engineering, Southern Methodist University, 3145 Dyer Street, Dallas, TX, 75205, USA
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Yu P, Li D, Ni J, Zhao L, Ding G, Wang Z, Xiao W. Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity. Chem Biol Drug Des 2017; 91:867-873. [DOI: 10.1111/cbdd.13150] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 08/11/2017] [Accepted: 09/13/2017] [Indexed: 12/26/2022]
Affiliation(s)
- Pan Yu
- Co-Innovation Center for Sustainable Forestry in Southern China; Nanjing Forestry University; Nanjing China
- College of Chemical Engineering; Nanjing Forestry University; Nanjing China
| | - Dongdong Li
- Co-Innovation Center for Sustainable Forestry in Southern China; Nanjing Forestry University; Nanjing China
- College of Chemical Engineering; Nanjing Forestry University; Nanjing China
| | - Junjun Ni
- College of Chemical Engineering; Nanjing Forestry University; Nanjing China
| | - Linguo Zhao
- Co-Innovation Center for Sustainable Forestry in Southern China; Nanjing Forestry University; Nanjing China
- College of Chemical Engineering; Nanjing Forestry University; Nanjing China
| | - Gang Ding
- Jiangsu Kanion Pharmaceutical Co., Ltd.; Lianyungang Jiangsu Province China
| | - Zhenzhong Wang
- Jiangsu Kanion Pharmaceutical Co., Ltd.; Lianyungang Jiangsu Province China
| | - Wei Xiao
- Jiangsu Kanion Pharmaceutical Co., Ltd.; Lianyungang Jiangsu Province China
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Cavalluzzi MM, Mangiatordi GF, Nicolotti O, Lentini G. Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective. Expert Opin Drug Discov 2017; 12:1087-1104. [PMID: 28814111 DOI: 10.1080/17460441.2017.1365056] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.
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Affiliation(s)
| | | | - Orazio Nicolotti
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
| | - Giovanni Lentini
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
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Shen W, Xiao T, Chen S, Liu F, Chen YZ, Jiang Y. Predicting the Enzymatic Hydrolysis Half‐lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination. Mol Inform 2017. [DOI: 10.1002/minf.201600153] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Wanxiang Shen
- Department of ChemistryTsinghua University Beijing 100084 P. R. China
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
| | - Tao Xiao
- Department of ChemistryTsinghua University Beijing 100084 P. R. China
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
| | - Shangying Chen
- Bioinformatics and Drug Design Group, Department of PharmacyNational University of Singapore Singapore 117543 Singapore
| | - Feng Liu
- Department of ChemistryTsinghua University Beijing 100084 P. R. China
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
| | - Yu Zong Chen
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
- Bioinformatics and Drug Design Group, Department of PharmacyNational University of Singapore Singapore 117543 Singapore
- Shenzhen Kivita Innovative Drug Discovery Institute Shenzhen 518055 P. R. China
| | - Yuyang Jiang
- The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at ShenzhenTsinghua University Shenzhen 518055 P. R. China
- School of Pharmaceutical SciencesTsinghua University Beijing 100084 P. R. China
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Sheridan RP. Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities. J Chem Inf Model 2016; 56:2253-2262. [DOI: 10.1021/acs.jcim.6b00431] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Robert P. Sheridan
- Modeling and Informatics Department, Merck & Co. Inc., Rahway, New Jersey 07065, United States
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Cortes-Ciriano I. Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR. J Chem Inf Model 2016; 56:1576-87. [DOI: 10.1021/acs.jcim.6b00136] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Isidro Cortes-Ciriano
- Département de Biologie
Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3825, 25, rue du Dr Roux, 75015 Paris, France
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Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. J Cheminform 2015; 7:63. [PMID: 26719774 PMCID: PMC4696267 DOI: 10.1186/s13321-015-0110-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 12/02/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. RESULTS Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. CONCLUSIONS We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/.
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Li J, Bai F, Liu H, Gramatica P. Ligand Efficiency Outperforms pIC50on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists. Chem Biol Drug Des 2015. [PMID: 26198098 DOI: 10.1111/cbdd.12619] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Jiazhong Li
- School of Pharmacy; Lanzhou University; 199 West Donggang Road 730000 Lanzhou China
- The Separating Scientific Institute of Lanzhou; 3 Weiyi Road 730000 Lanzhou China
| | - Fang Bai
- School of Pharmacy; Lanzhou University; 199 West Donggang Road 730000 Lanzhou China
| | - Huanxiang Liu
- School of Pharmacy; Lanzhou University; 199 West Donggang Road 730000 Lanzhou China
| | - Paola Gramatica
- Department of Theoretical and Applied Sciences; University of Insubria; via Dunant 3 21100 Varese Italy
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Wang T, Wu MB, Lin JP, Yang LR. Quantitative structure–activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discov 2015; 10:1283-300. [DOI: 10.1517/17460441.2015.1083006] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Clustering molecular dynamics trajectories for optimizing docking experiments. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:916240. [PMID: 25873944 PMCID: PMC4385651 DOI: 10.1155/2015/916240] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 03/05/2015] [Indexed: 12/03/2022]
Abstract
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
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